{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "cf629664",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-29T07:31:34.571650Z",
     "start_time": "2024-06-29T07:31:34.520082Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "fa98d50a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-29T07:31:35.551910Z",
     "start_time": "2024-06-29T07:31:35.204194Z"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('merged_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3d7dca39",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-29T07:34:22.139875Z",
     "start_time": "2024-06-29T07:34:22.128320Z"
    }
   },
   "outputs": [],
   "source": [
    "grouped_stats = df.groupby('hplx_y')['jz'].agg(['sum'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "2b1db71a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-29T07:34:22.949651Z",
     "start_time": "2024-06-29T07:34:22.939054Z"
    }
   },
   "outputs": [],
   "source": [
    "grouped_stats = grouped_stats.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "fdb03c63",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-29T07:34:24.352296Z",
     "start_time": "2024-06-29T07:34:24.343769Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   hplx_y         sum\n",
      "0       0   786481.83\n",
      "1       1  1317651.34\n"
     ]
    }
   ],
   "source": [
    "print(grouped_stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d7ef5dfe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-29T07:34:30.373990Z",
     "start_time": "2024-06-29T07:34:30.353084Z"
    }
   },
   "outputs": [],
   "source": [
    "grouped_stats.to_csv('销售量分析表', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "548c87b1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-29T07:34:33.691255Z",
     "start_time": "2024-06-29T07:34:33.535108Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Sales volume')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grouped_stats.plot(kind='bar')\n",
    "plt.title('Comparison chart of sales volume')  # 设置图表标题\n",
    "plt.xlabel('Type of goods')  # 设置X轴标签\n",
    "plt.ylabel('Sales volume')  # 设置Y轴标签"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d33e2409",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a71979fd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
